Overview

Dataset statistics

Number of variables24
Number of observations159256
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.2 MiB
Average record size in memory192.0 B

Variable types

Numeric20
Categorical4

Alerts

age is highly overall correlated with height(cm)High correlation
height(cm) is highly overall correlated with age and 2 other fieldsHigh correlation
weight(kg) is highly overall correlated with height(cm) and 2 other fieldsHigh correlation
waist(cm) is highly overall correlated with weight(kg)High correlation
eyesight(left) is highly overall correlated with eyesight(right)High correlation
eyesight(right) is highly overall correlated with eyesight(left)High correlation
systolic is highly overall correlated with relaxationHigh correlation
relaxation is highly overall correlated with systolicHigh correlation
Cholesterol is highly overall correlated with LDLHigh correlation
triglyceride is highly overall correlated with HDLHigh correlation
HDL is highly overall correlated with triglycerideHigh correlation
LDL is highly overall correlated with CholesterolHigh correlation
hemoglobin is highly overall correlated with height(cm) and 1 other fieldsHigh correlation
AST is highly overall correlated with ALTHigh correlation
ALT is highly overall correlated with AST and 1 other fieldsHigh correlation
Gtp is highly overall correlated with ALTHigh correlation
hearing(left) is highly overall correlated with hearing(right)High correlation
hearing(right) is highly overall correlated with hearing(left)High correlation
hearing(left) is highly imbalanced (83.7%)Imbalance
hearing(right) is highly imbalanced (84.0%)Imbalance
ALT is highly skewed (γ1 = 34.83001193)Skewed
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-11-05 17:42:41.644652
Analysis finished2023-11-05 17:43:45.474917
Duration1 minute and 3.83 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct159256
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79627.5
Minimum0
Maximum159255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:45.610921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7962.75
Q139813.75
median79627.5
Q3119441.25
95-th percentile151292.25
Maximum159255
Range159255
Interquartile range (IQR)79627.5

Descriptive statistics

Standard deviation45973.392
Coefficient of variation (CV)0.57735571
Kurtosis-1.2
Mean79627.5
Median Absolute Deviation (MAD)39814
Skewness0
Sum1.2681157 × 1010
Variance2.1135527 × 109
MonotonicityStrictly increasing
2023-11-05T23:13:45.790401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
106173 1
 
< 0.1%
106166 1
 
< 0.1%
106167 1
 
< 0.1%
106168 1
 
< 0.1%
106169 1
 
< 0.1%
106170 1
 
< 0.1%
106171 1
 
< 0.1%
106172 1
 
< 0.1%
106174 1
 
< 0.1%
Other values (159246) 159246
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
159255 1
< 0.1%
159254 1
< 0.1%
159253 1
< 0.1%
159252 1
< 0.1%
159251 1
< 0.1%
159250 1
< 0.1%
159249 1
< 0.1%
159248 1
< 0.1%
159247 1
< 0.1%
159246 1
< 0.1%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.306626
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:45.932692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q140
median40
Q355
95-th percentile65
Maximum85
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.842286
Coefficient of variation (CV)0.26728025
Kurtosis-0.15879716
Mean44.306626
Median Absolute Deviation (MAD)10
Skewness0.29169735
Sum7056096
Variance140.23973
MonotonicityNot monotonic
2023-11-05T23:13:46.082011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
40 46691
29.3%
60 19043
12.0%
45 18480
 
11.6%
50 15768
 
9.9%
55 13446
 
8.4%
35 13081
 
8.2%
30 11465
 
7.2%
25 9140
 
5.7%
20 3829
 
2.4%
65 3637
 
2.3%
Other values (8) 4676
 
2.9%
ValueCountFrequency (%)
20 3829
 
2.4%
25 9140
 
5.7%
30 11465
 
7.2%
35 13081
 
8.2%
40 46691
29.3%
45 18480
 
11.6%
49 1
 
< 0.1%
50 15768
 
9.9%
55 13446
 
8.4%
58 2
 
< 0.1%
ValueCountFrequency (%)
85 38
 
< 0.1%
80 644
 
0.4%
75 1760
 
1.1%
70 2229
 
1.4%
69 1
 
< 0.1%
65 3637
 
2.3%
62 1
 
< 0.1%
60 19043
12.0%
58 2
 
< 0.1%
55 13446
8.4%

height(cm)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.26693
Minimum135
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:46.200768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile150
Q1160
median165
Q3170
95-th percentile180
Maximum190
Range55
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.8189698
Coefficient of variation (CV)0.053361976
Kurtosis-0.53077328
Mean165.26693
Median Absolute Deviation (MAD)5
Skewness-0.26991481
Sum26319750
Variance77.774229
MonotonicityNot monotonic
2023-11-05T23:13:46.320405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
170 37398
23.5%
165 30145
18.9%
175 24852
15.6%
160 22783
14.3%
155 19597
12.3%
150 11534
 
7.2%
180 8185
 
5.1%
145 2736
 
1.7%
185 1562
 
1.0%
140 410
 
0.3%
Other values (4) 54
 
< 0.1%
ValueCountFrequency (%)
135 8
 
< 0.1%
139 1
 
< 0.1%
140 410
 
0.3%
145 2736
 
1.7%
150 11534
 
7.2%
155 19597
12.3%
160 22783
14.3%
165 30145
18.9%
170 37398
23.5%
175 24852
15.6%
ValueCountFrequency (%)
190 44
 
< 0.1%
185 1562
 
1.0%
181 1
 
< 0.1%
180 8185
 
5.1%
175 24852
15.6%
170 37398
23.5%
165 30145
18.9%
160 22783
14.3%
155 19597
12.3%
150 11534
 
7.2%

weight(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.143662
Minimum30
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:46.456237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile50
Q160
median65
Q375
95-th percentile90
Maximum130
Range100
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.586198
Coefficient of variation (CV)0.18745177
Kurtosis-0.036576272
Mean67.143662
Median Absolute Deviation (MAD)10
Skewness0.37807475
Sum10693031
Variance158.41238
MonotonicityNot monotonic
2023-11-05T23:13:46.597888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
70 24168
15.2%
65 23069
14.5%
60 21658
13.6%
75 19698
12.4%
55 18715
11.8%
50 13780
8.7%
80 13631
8.6%
85 8612
 
5.4%
45 5420
 
3.4%
90 4676
 
2.9%
Other values (18) 5829
 
3.7%
ValueCountFrequency (%)
30 14
 
< 0.1%
35 47
 
< 0.1%
40 881
 
0.6%
45 5420
 
3.4%
50 13780
8.7%
55 18715
11.8%
60 21658
13.6%
65 23069
14.5%
70 24168
15.2%
75 19698
12.4%
ValueCountFrequency (%)
130 2
 
< 0.1%
125 6
 
< 0.1%
120 24
 
< 0.1%
115 82
 
0.1%
110 228
 
0.1%
105 500
 
0.3%
101 1
 
< 0.1%
100 1391
0.9%
99 1
 
< 0.1%
95 2647
1.7%

waist(cm)
Real number (ℝ)

HIGH CORRELATION 

Distinct531
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.00199
Minimum51
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:46.741841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile68
Q177
median83
Q389
95-th percentile98
Maximum127
Range76
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9579373
Coefficient of variation (CV)0.10792437
Kurtosis-0.056410967
Mean83.00199
Median Absolute Deviation (MAD)6
Skewness0.072445894
Sum13218565
Variance80.24464
MonotonicityNot monotonic
2023-11-05T23:13:46.956310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 6572
 
4.1%
86 6026
 
3.8%
84 5986
 
3.8%
82 5962
 
3.7%
81 5619
 
3.5%
87 5581
 
3.5%
85 5488
 
3.4%
83 5354
 
3.4%
88 5048
 
3.2%
78 4819
 
3.0%
Other values (521) 102801
64.6%
ValueCountFrequency (%)
51 4
 
< 0.1%
56 5
 
< 0.1%
56.2 1
 
< 0.1%
56.3 1
 
< 0.1%
56.5 1
 
< 0.1%
57 5
 
< 0.1%
57.4 3
 
< 0.1%
57.5 1
 
< 0.1%
57.7 4
 
< 0.1%
58 42
< 0.1%
ValueCountFrequency (%)
127 3
< 0.1%
125.8 1
 
< 0.1%
124 2
 
< 0.1%
123 1
 
< 0.1%
121.4 1
 
< 0.1%
121 4
< 0.1%
120.5 1
 
< 0.1%
120 3
< 0.1%
118.2 1
 
< 0.1%
118 5
< 0.1%

eyesight(left)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0057982
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:47.148502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.40211261
Coefficient of variation (CV)0.39979452
Kurtosis196.46547
Mean1.0057982
Median Absolute Deviation (MAD)0.2
Skewness8.8905724
Sum160179.4
Variance0.16169455
MonotonicityNot monotonic
2023-11-05T23:13:47.287504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 37380
23.5%
1.2 36618
23.0%
1.5 21550
13.5%
0.9 15212
9.6%
0.8 15078
9.5%
0.7 13124
 
8.2%
0.6 6769
 
4.3%
0.5 5599
 
3.5%
0.4 3337
 
2.1%
0.3 2235
 
1.4%
Other values (10) 2354
 
1.5%
ValueCountFrequency (%)
0.1 710
 
0.4%
0.2 922
 
0.6%
0.3 2235
 
1.4%
0.4 3337
 
2.1%
0.5 5599
 
3.5%
0.6 6769
 
4.3%
0.7 13124
 
8.2%
0.8 15078
9.5%
0.9 15212
9.6%
1 37380
23.5%
ValueCountFrequency (%)
9.9 132
 
0.1%
2 557
 
0.3%
1.9 1
 
< 0.1%
1.8 2
 
< 0.1%
1.7 1
 
< 0.1%
1.6 19
 
< 0.1%
1.5 21550
13.5%
1.3 9
 
< 0.1%
1.2 36618
23.0%
1.1 1
 
< 0.1%

eyesight(right)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.000989
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:47.421442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.39229858
Coefficient of variation (CV)0.39191099
Kurtosis192.72935
Mean1.000989
Median Absolute Deviation (MAD)0.2
Skewness8.4868491
Sum159413.5
Variance0.15389818
MonotonicityNot monotonic
2023-11-05T23:13:47.550380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 38014
23.9%
1.2 36302
22.8%
1.5 20813
13.1%
0.9 15719
9.9%
0.8 15505
9.7%
0.7 12144
 
7.6%
0.6 6708
 
4.2%
0.5 5838
 
3.7%
0.4 3580
 
2.2%
0.3 2116
 
1.3%
Other values (7) 2517
 
1.6%
ValueCountFrequency (%)
0.1 803
 
0.5%
0.2 1037
 
0.7%
0.3 2116
 
1.3%
0.4 3580
 
2.2%
0.5 5838
 
3.7%
0.6 6708
 
4.2%
0.7 12144
 
7.6%
0.8 15505
9.7%
0.9 15719
9.9%
1 38014
23.9%
ValueCountFrequency (%)
9.9 117
 
0.1%
2 536
 
0.3%
1.6 19
 
< 0.1%
1.5 20813
13.1%
1.4 2
 
< 0.1%
1.3 3
 
< 0.1%
1.2 36302
22.8%
1 38014
23.9%
0.9 15719
9.9%
0.8 15505
9.7%

hearing(left)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
155438 
2
 
3818

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159256
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 155438
97.6%
2 3818
 
2.4%

Length

2023-11-05T23:13:47.692221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T23:13:47.826103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 155438
97.6%
2 3818
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 155438
97.6%
2 3818
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155438
97.6%
2 3818
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 159256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155438
97.6%
2 3818
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155438
97.6%
2 3818
 
2.4%

hearing(right)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
155526 
2
 
3730

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159256
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 155526
97.7%
2 3730
 
2.3%

Length

2023-11-05T23:13:47.949940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T23:13:48.058906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 155526
97.7%
2 3730
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 155526
97.7%
2 3730
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155526
97.7%
2 3730
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 159256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155526
97.7%
2 3730
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155526
97.7%
2 3730
 
2.3%

systolic
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.50365
Minimum77
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:48.212011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile101
Q1114
median121
Q3130
95-th percentile144
Maximum213
Range136
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.729315
Coefficient of variation (CV)0.10390968
Kurtosis0.21205237
Mean122.50365
Median Absolute Deviation (MAD)9
Skewness0.21992481
Sum19509441
Variance162.03546
MonotonicityNot monotonic
2023-11-05T23:13:48.393511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 11765
 
7.4%
120 11749
 
7.4%
110 11591
 
7.3%
118 9686
 
6.1%
124 4692
 
2.9%
128 4528
 
2.8%
116 4517
 
2.8%
138 4512
 
2.8%
122 4163
 
2.6%
119 3967
 
2.5%
Other values (102) 88086
55.3%
ValueCountFrequency (%)
77 1
 
< 0.1%
79 1
 
< 0.1%
80 2
 
< 0.1%
81 6
 
< 0.1%
82 3
 
< 0.1%
83 6
 
< 0.1%
84 1
 
< 0.1%
85 1
 
< 0.1%
86 20
< 0.1%
87 17
< 0.1%
ValueCountFrequency (%)
213 1
 
< 0.1%
203 2
 
< 0.1%
199 2
 
< 0.1%
198 1
 
< 0.1%
195 2
 
< 0.1%
192 1
 
< 0.1%
190 8
< 0.1%
185 1
 
< 0.1%
184 1
 
< 0.1%
183 1
 
< 0.1%

relaxation
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.874071
Minimum44
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:48.570114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile61
Q170
median78
Q382
95-th percentile91
Maximum133
Range89
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9946417
Coefficient of variation (CV)0.11700488
Kurtosis0.14220139
Mean76.874071
Median Absolute Deviation (MAD)6
Skewness0.17634977
Sum12242657
Variance80.903579
MonotonicityNot monotonic
2023-11-05T23:13:48.735049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 20326
 
12.8%
70 16878
 
10.6%
78 10258
 
6.4%
74 5924
 
3.7%
72 5875
 
3.7%
76 5568
 
3.5%
82 5385
 
3.4%
88 5345
 
3.4%
75 5233
 
3.3%
60 5006
 
3.1%
Other values (65) 73458
46.1%
ValueCountFrequency (%)
44 2
 
< 0.1%
46 3
 
< 0.1%
47 1
 
< 0.1%
48 6
 
< 0.1%
49 3
 
< 0.1%
50 37
 
< 0.1%
51 81
0.1%
52 49
 
< 0.1%
53 67
< 0.1%
54 160
0.1%
ValueCountFrequency (%)
133 1
 
< 0.1%
122 1
 
< 0.1%
121 1
 
< 0.1%
120 19
 
< 0.1%
116 4
 
< 0.1%
114 7
 
< 0.1%
113 5
 
< 0.1%
112 6
 
< 0.1%
111 6
 
< 0.1%
110 153
0.1%

fasting blood sugar
Real number (ℝ)

Distinct229
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.352552
Minimum46
Maximum375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:48.894953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile81
Q190
median96
Q3103
95-th percentile122
Maximum375
Range329
Interquartile range (IQR)13

Descriptive statistics

Standard deviation15.32974
Coefficient of variation (CV)0.1558652
Kurtosis27.200004
Mean98.352552
Median Absolute Deviation (MAD)7
Skewness3.461099
Sum15663234
Variance235.00093
MonotonicityNot monotonic
2023-11-05T23:13:49.919014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 7109
 
4.5%
97 6733
 
4.2%
92 6615
 
4.2%
95 6585
 
4.1%
91 6461
 
4.1%
93 6460
 
4.1%
98 6081
 
3.8%
99 6058
 
3.8%
96 6019
 
3.8%
90 5704
 
3.6%
Other values (219) 95431
59.9%
ValueCountFrequency (%)
46 1
 
< 0.1%
48 2
 
< 0.1%
51 3
 
< 0.1%
55 3
 
< 0.1%
56 4
 
< 0.1%
57 2
 
< 0.1%
59 5
 
< 0.1%
60 8
< 0.1%
61 15
< 0.1%
62 1
 
< 0.1%
ValueCountFrequency (%)
375 2
 
< 0.1%
369 4
< 0.1%
365 1
 
< 0.1%
318 1
 
< 0.1%
313 4
< 0.1%
308 1
 
< 0.1%
303 1
 
< 0.1%
302 6
< 0.1%
297 1
 
< 0.1%
288 2
 
< 0.1%

Cholesterol
Real number (ℝ)

HIGH CORRELATION 

Distinct227
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.79616
Minimum77
Maximum393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:50.074381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile150
Q1175
median196
Q3217
95-th percentile240
Maximum393
Range316
Interquartile range (IQR)42

Descriptive statistics

Standard deviation28.396959
Coefficient of variation (CV)0.14503328
Kurtosis-0.20628097
Mean195.79616
Median Absolute Deviation (MAD)21
Skewness0.043487789
Sum31181714
Variance806.38729
MonotonicityNot monotonic
2023-11-05T23:13:50.235363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197 2238
 
1.4%
198 2232
 
1.4%
216 2203
 
1.4%
199 2194
 
1.4%
192 2149
 
1.3%
220 2139
 
1.3%
201 2138
 
1.3%
204 2134
 
1.3%
207 2113
 
1.3%
203 2079
 
1.3%
Other values (217) 137637
86.4%
ValueCountFrequency (%)
77 1
 
< 0.1%
91 4
< 0.1%
96 1
 
< 0.1%
98 1
 
< 0.1%
99 2
< 0.1%
100 3
< 0.1%
101 2
< 0.1%
102 3
< 0.1%
103 3
< 0.1%
104 3
< 0.1%
ValueCountFrequency (%)
393 1
 
< 0.1%
366 1
 
< 0.1%
351 2
< 0.1%
347 2
< 0.1%
344 1
 
< 0.1%
330 2
< 0.1%
322 4
< 0.1%
320 1
 
< 0.1%
318 4
< 0.1%
317 1
 
< 0.1%

triglyceride
Real number (ℝ)

HIGH CORRELATION 

Distinct392
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.61605
Minimum8
Maximum766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:50.401528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile44
Q177
median115
Q3165
95-th percentile255
Maximum766
Range758
Interquartile range (IQR)88

Descriptive statistics

Standard deviation66.188989
Coefficient of variation (CV)0.51865726
Kurtosis0.99610845
Mean127.61605
Median Absolute Deviation (MAD)43
Skewness0.98622775
Sum20323621
Variance4380.9823
MonotonicityNot monotonic
2023-11-05T23:13:50.567931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 1555
 
1.0%
147 1548
 
1.0%
85 1505
 
0.9%
71 1476
 
0.9%
79 1404
 
0.9%
86 1346
 
0.8%
45 1283
 
0.8%
88 1278
 
0.8%
67 1270
 
0.8%
105 1269
 
0.8%
Other values (382) 145322
91.3%
ValueCountFrequency (%)
8 2
 
< 0.1%
11 2
 
< 0.1%
14 2
 
< 0.1%
15 1
 
< 0.1%
16 9
 
< 0.1%
17 2
 
< 0.1%
18 1
 
< 0.1%
19 2
 
< 0.1%
20 24
< 0.1%
21 12
< 0.1%
ValueCountFrequency (%)
766 1
 
< 0.1%
548 1
 
< 0.1%
466 2
 
< 0.1%
432 2
 
< 0.1%
399 39
< 0.1%
398 2
 
< 0.1%
397 46
< 0.1%
396 4
 
< 0.1%
395 3
 
< 0.1%
394 28
< 0.1%

HDL
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.852684
Minimum9
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:50.737715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile37
Q145
median54
Q364
95-th percentile83
Maximum136
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.964141
Coefficient of variation (CV)0.25001737
Kurtosis0.30332561
Mean55.852684
Median Absolute Deviation (MAD)9
Skewness0.75888408
Sum8894875
Variance194.99724
MonotonicityNot monotonic
2023-11-05T23:13:50.900919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 5431
 
3.4%
46 5200
 
3.3%
44 4983
 
3.1%
48 4968
 
3.1%
49 4939
 
3.1%
54 4917
 
3.1%
51 4852
 
3.0%
50 4791
 
3.0%
52 4528
 
2.8%
55 4464
 
2.8%
Other values (98) 110183
69.2%
ValueCountFrequency (%)
9 1
 
< 0.1%
18 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
< 0.1%
24 6
 
< 0.1%
25 11
 
< 0.1%
26 10
 
< 0.1%
27 21
 
< 0.1%
28 60
< 0.1%
29 55
< 0.1%
ValueCountFrequency (%)
136 1
 
< 0.1%
135 2
 
< 0.1%
133 4
 
< 0.1%
125 7
< 0.1%
123 1
 
< 0.1%
122 6
< 0.1%
121 11
< 0.1%
120 4
 
< 0.1%
119 12
< 0.1%
118 1
 
< 0.1%

LDL
Real number (ℝ)

HIGH CORRELATION 

Distinct222
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.60768
Minimum1
Maximum1860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:51.082385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile74
Q195
median114
Q3133
95-th percentile157
Maximum1860
Range1859
Interquartile range (IQR)38

Descriptive statistics

Standard deviation28.158931
Coefficient of variation (CV)0.24569846
Kurtosis323.6313
Mean114.60768
Median Absolute Deviation (MAD)19
Skewness6.8748102
Sum18251961
Variance792.92542
MonotonicityNot monotonic
2023-11-05T23:13:51.265561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 2515
 
1.6%
106 2495
 
1.6%
121 2360
 
1.5%
116 2341
 
1.5%
107 2304
 
1.4%
127 2289
 
1.4%
114 2285
 
1.4%
128 2214
 
1.4%
118 2209
 
1.4%
123 2199
 
1.4%
Other values (212) 136045
85.4%
ValueCountFrequency (%)
1 2
 
< 0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
17 2
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 5
< 0.1%
22 5
< 0.1%
23 1
 
< 0.1%
ValueCountFrequency (%)
1860 1
 
< 0.1%
1660 2
< 0.1%
1220 3
< 0.1%
1200 2
< 0.1%
1120 2
< 0.1%
1070 2
< 0.1%
1010 1
 
< 0.1%
790 1
 
< 0.1%
318 1
 
< 0.1%
292 1
 
< 0.1%

hemoglobin
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.796965
Minimum4.9
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:51.436786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile12.4
Q113.8
median15
Q315.8
95-th percentile16.8
Maximum21
Range16.1
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4312131
Coefficient of variation (CV)0.096723419
Kurtosis0.95852579
Mean14.796965
Median Absolute Deviation (MAD)1
Skewness-0.63357056
Sum2356505.5
Variance2.0483709
MonotonicityNot monotonic
2023-11-05T23:13:51.599602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.6 5391
 
3.4%
15.7 4981
 
3.1%
15.5 4968
 
3.1%
15.4 4929
 
3.1%
15.8 4881
 
3.1%
15.9 4848
 
3.0%
15 4686
 
2.9%
15.3 4651
 
2.9%
14.9 4310
 
2.7%
15.2 4306
 
2.7%
Other values (124) 111305
69.9%
ValueCountFrequency (%)
4.9 1
 
< 0.1%
5.8 5
 
< 0.1%
5.9 1
 
< 0.1%
6.3 3
 
< 0.1%
6.4 1
 
< 0.1%
6.6 8
< 0.1%
6.9 6
< 0.1%
7 3
 
< 0.1%
7.1 13
< 0.1%
7.2 9
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20.4 2
 
< 0.1%
19.7 1
 
< 0.1%
19.3 3
 
< 0.1%
19.2 4
 
< 0.1%
19.1 8
 
< 0.1%
19 15
< 0.1%
18.9 4
 
< 0.1%
18.8 34
< 0.1%
18.7 18
< 0.1%

Urine protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0742327
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:51.733957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34785565
Coefficient of variation (CV)0.32381779
Kurtosis35.953698
Mean1.0742327
Median Absolute Deviation (MAD)0
Skewness5.5855624
Sum171078
Variance0.12100356
MonotonicityNot monotonic
2023-11-05T23:13:51.851521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 150862
94.7%
2 5609
 
3.5%
3 2228
 
1.4%
4 477
 
0.3%
5 74
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
1 150862
94.7%
2 5609
 
3.5%
3 2228
 
1.4%
4 477
 
0.3%
5 74
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
6 6
 
< 0.1%
5 74
 
< 0.1%
4 477
 
0.3%
3 2228
 
1.4%
2 5609
 
3.5%
1 150862
94.7%

serum creatinine
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89276448
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:51.979780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile1.2
Maximum9.9
Range9.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.17934595
Coefficient of variation (CV)0.20088832
Kurtosis58.931478
Mean0.89276448
Median Absolute Deviation (MAD)0.1
Skewness1.5110591
Sum142178.1
Variance0.032164971
MonotonicityNot monotonic
2023-11-05T23:13:52.117886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.9 35776
22.5%
1 31313
19.7%
0.8 29802
18.7%
0.7 19782
12.4%
1.1 17830
11.2%
0.6 11153
 
7.0%
1.2 7657
 
4.8%
0.5 2624
 
1.6%
1.3 2123
 
1.3%
1.4 604
 
0.4%
Other values (18) 592
 
0.4%
ValueCountFrequency (%)
0.1 17
 
< 0.1%
0.2 3
 
< 0.1%
0.3 5
 
< 0.1%
0.4 311
 
0.2%
0.5 2624
 
1.6%
0.6 11153
 
7.0%
0.7 19782
12.4%
0.8 29802
18.7%
0.9 35776
22.5%
1 31313
19.7%
ValueCountFrequency (%)
9.9 1
 
< 0.1%
7.4 1
 
< 0.1%
5.9 2
 
< 0.1%
3.4 1
 
< 0.1%
3 1
 
< 0.1%
2.5 3
 
< 0.1%
2.2 3
 
< 0.1%
2.1 4
 
< 0.1%
2 7
< 0.1%
1.9 12
< 0.1%

AST
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.516853
Minimum6
Maximum778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:52.268523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile16
Q120
median24
Q329
95-th percentile41
Maximum778
Range772
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.4648821
Coefficient of variation (CV)0.37092669
Kurtosis629.51933
Mean25.516853
Median Absolute Deviation (MAD)5
Skewness11.592346
Sum4063712
Variance89.583993
MonotonicityNot monotonic
2023-11-05T23:13:52.428236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 10628
 
6.7%
21 10600
 
6.7%
23 10371
 
6.5%
22 10361
 
6.5%
24 9827
 
6.2%
19 9759
 
6.1%
25 8347
 
5.2%
18 8340
 
5.2%
26 7445
 
4.7%
17 6697
 
4.2%
Other values (130) 66881
42.0%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 19
 
< 0.1%
10 52
 
< 0.1%
11 145
 
0.1%
12 472
 
0.3%
13 856
 
0.5%
14 1944
1.2%
15 3395
2.1%
ValueCountFrequency (%)
778 1
< 0.1%
656 1
< 0.1%
591 2
< 0.1%
527 1
< 0.1%
387 1
< 0.1%
326 1
< 0.1%
320 1
< 0.1%
311 1
< 0.1%
250 1
< 0.1%
221 1
< 0.1%

ALT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct188
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.550296
Minimum1
Maximum2914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:52.597064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q116
median22
Q332
95-th percentile55
Maximum2914
Range2913
Interquartile range (IQR)16

Descriptive statistics

Standard deviation17.75307
Coefficient of variation (CV)0.66865808
Kurtosis4870.2419
Mean26.550296
Median Absolute Deviation (MAD)7
Skewness34.830012
Sum4228294
Variance315.1715
MonotonicityNot monotonic
2023-11-05T23:13:52.753435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 7337
 
4.6%
16 7226
 
4.5%
18 6890
 
4.3%
17 6805
 
4.3%
19 6757
 
4.2%
20 6592
 
4.1%
14 6288
 
3.9%
21 6154
 
3.9%
22 5884
 
3.7%
13 5777
 
3.6%
Other values (178) 93546
58.7%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 24
 
< 0.1%
5 55
 
< 0.1%
6 146
 
0.1%
7 391
 
0.2%
8 926
 
0.6%
9 1597
1.0%
10 2886
1.8%
ValueCountFrequency (%)
2914 1
< 0.1%
1612 1
< 0.1%
745 2
< 0.1%
740 1
< 0.1%
713 1
< 0.1%
610 1
< 0.1%
280 1
< 0.1%
250 1
< 0.1%
224 1
< 0.1%
220 2
< 0.1%

Gtp
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.216004
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-11-05T23:13:52.931579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q118
median27
Q344
95-th percentile88
Maximum999
Range997
Interquartile range (IQR)26

Descriptive statistics

Standard deviation31.204643
Coefficient of variation (CV)0.86162577
Kurtosis69.558428
Mean36.216004
Median Absolute Deviation (MAD)11
Skewness5.4148273
Sum5767616
Variance973.72973
MonotonicityNot monotonic
2023-11-05T23:13:53.117300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 5473
 
3.4%
16 5415
 
3.4%
15 5310
 
3.3%
18 5307
 
3.3%
19 5087
 
3.2%
17 5065
 
3.2%
20 4820
 
3.0%
21 4795
 
3.0%
13 4737
 
3.0%
22 4299
 
2.7%
Other values (352) 108948
68.4%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 2
 
< 0.1%
5 17
 
< 0.1%
6 35
 
< 0.1%
7 144
 
0.1%
8 377
 
0.2%
9 1081
 
0.7%
10 1974
1.2%
11 2995
1.9%
12 3605
2.3%
ValueCountFrequency (%)
999 1
< 0.1%
926 1
< 0.1%
836 1
< 0.1%
816 1
< 0.1%
778 2
< 0.1%
766 1
< 0.1%
764 2
< 0.1%
752 2
< 0.1%
691 2
< 0.1%
667 1
< 0.1%

dental caries
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
127724 
1
31532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159256
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 127724
80.2%
1 31532
 
19.8%

Length

2023-11-05T23:13:53.266382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T23:13:53.390380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 127724
80.2%
1 31532
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 127724
80.2%
1 31532
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127724
80.2%
1 31532
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 159256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127724
80.2%
1 31532
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127724
80.2%
1 31532
 
19.8%

smoking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
89603 
1
69653 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159256
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 89603
56.3%
1 69653
43.7%

Length

2023-11-05T23:13:53.518977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T23:13:53.633643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 89603
56.3%
1 69653
43.7%

Most occurring characters

ValueCountFrequency (%)
0 89603
56.3%
1 69653
43.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89603
56.3%
1 69653
43.7%

Most occurring scripts

ValueCountFrequency (%)
Common 159256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89603
56.3%
1 69653
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89603
56.3%
1 69653
43.7%

Interactions

2023-11-05T23:13:41.787772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:50.487211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.141483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.111214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.760472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:01.243215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.165499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.699625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:09.242542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.153994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.643730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.232240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.794423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.915330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.425303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.998615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.573635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.619778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.572557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.321557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.913523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:50.652752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.273518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.267911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.888034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:01.683335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.297871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.838348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:09.377431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.295525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.780812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.372600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.931149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.045370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.558217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.132247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.706399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.757819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.709211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.451562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.034504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:50.838634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.416766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.390677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.009517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:01.814925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.418299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.963348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:09.501528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.429819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.907042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.496415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:20.060298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.167039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.686457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.270263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.824316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.892400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.834694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.577631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.150037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:50.986515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.569968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.512713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.124717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:01.941938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.546078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.085068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:09.944632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.547116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.033572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.623993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:20.599412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.284218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.820298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.397785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.948383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:34.075793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.956097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.712835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.261522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.120806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.700895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.628700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.243413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.061822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.670585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.202013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.060586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.657557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.161148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.744954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:20.721835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.400887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.945326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.519053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.062446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:34.222429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.077737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.828427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.388069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.258943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.830702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.760221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.371294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.192317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.803437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.336621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.195029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.787156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.301363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.880969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:20.861258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.529053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.084467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.653976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.196350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:34.368476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.215363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.957752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.500225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.380450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.966941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.877164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.487758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.320497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.922503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.457371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.314372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.900214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.428037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.003252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:20.987935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.649588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.207909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.776725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.313902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:34.572579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.338158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.073618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.616243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.497615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:54.121519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:56.997674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.602984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.450013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.038940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.578763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.438582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.019101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.552713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.123812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.116503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.781481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.330659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:28.900124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.434940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:34.736944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.460142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.192167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.739856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.618581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:54.462959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.131663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.727732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.579862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.164110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.699563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.562902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.149477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.683387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.253667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.247575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:23.901918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.456273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.035422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.563881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:34.927927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.593027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.315799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.862338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.734652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:54.571893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.259058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.841259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.705664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.282063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.824572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.682679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.259472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.810256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.380221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.370372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.020221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.581255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.154328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.677234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.105654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.724424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.431302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:42.982168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.865559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:54.691113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.387913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:59.967292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.838923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.406057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:07.954059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.811086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.383796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:15.939826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.504711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.502475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.149568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.709481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.287909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.803416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.261948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.850161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.555220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.101179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:51.986889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:54.819236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.512867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.092508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:02.973554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.532284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.081616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:10.945185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.509073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.067776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.628341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.637529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.278250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.837481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.416404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:31.925515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.412283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:37.977542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.683471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.228029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.118389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:54.956279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.661098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.227661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.111172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.672284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.215545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.078106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.634209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.202081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.767276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.773024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.414112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:26.975053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.552723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:32.060821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.557228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:38.112330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.818157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.372256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.239487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.079299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.805714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.367489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.243782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.794968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.342750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.203096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.753633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.331754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:18.894366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:21.901188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.534476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.099897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.678665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:32.189523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.681626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:38.279309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:40.943140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.520093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.364648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.205234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:57.938391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.493304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.375741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:05.922137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.470564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.329858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:13.878423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.460521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.024745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.035465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.664767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.228036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.805867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:32.316276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.804078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:38.451379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.062991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.652176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.495865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.335170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.098227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.620029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.517277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.051639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.599152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.459569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.006909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.598040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.152956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.172482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.801104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.360540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:29.936490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:32.441947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:35.930914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:38.637317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.188096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.789036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.616775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.475765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.233895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.748333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.642870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.169001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.718933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.577058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.133387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.721987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.275802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.316150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:24.926492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.480041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.069149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.117330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.044780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:38.824083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.302567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:43.959000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.735393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.653683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.370565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.863920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.775410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.305947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.854996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.724041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.264328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.845619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.402849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.452163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.048018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.602018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.192298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.250164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.172724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:38.944436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.421414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:44.117556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:52.869780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.809376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.518735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:00.992208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:03.910502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.447430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:08.988561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:11.866942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.399438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:16.982564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.547358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.599151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.180421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.739091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.324487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.382272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.320899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.073601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.545206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:44.243570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:53.008471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:55.950344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:12:58.639646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:01.116203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:04.040279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:06.571356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:09.126575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:12.016617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:14.526480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:17.110250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:19.674593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:22.758101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:25.306894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:27.868821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:30.450214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:33.499121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:36.448145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:39.200719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-05T23:13:41.665522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-05T23:13:53.749061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtphearing(left)hearing(right)dental cariessmoking
id1.000-0.0030.001-0.002-0.0030.0030.003-0.0000.0010.000-0.001-0.0010.004-0.0020.001-0.002-0.0030.002-0.0000.0020.0000.0040.0030.004
age-0.0031.000-0.509-0.356-0.063-0.378-0.3740.1410.0660.2060.120-0.0320.0590.124-0.324-0.025-0.1820.070-0.131-0.0760.2750.2780.1290.217
height(cm)0.001-0.5091.0000.7040.4160.2800.2840.0750.1180.037-0.1060.272-0.303-0.0780.586-0.0120.4820.0700.3000.3490.1050.1070.1030.483
weight(kg)-0.002-0.3560.7041.0000.8200.2090.2140.2450.2650.1760.0220.430-0.4440.0710.560-0.0360.4340.1820.4770.4780.0700.0700.0890.370
waist(cm)-0.003-0.0630.4160.8201.0000.0590.0650.3050.2880.2520.0800.440-0.4420.1340.419-0.0420.3110.2230.4680.4730.0270.0270.0520.267
eyesight(left)0.003-0.3780.2800.2090.0591.0000.712-0.0490.009-0.060-0.0150.069-0.051-0.0240.191-0.0010.126-0.0060.1020.0880.0780.0790.0310.106
eyesight(right)0.003-0.3740.2840.2140.0650.7121.000-0.0440.016-0.055-0.0120.079-0.057-0.0220.1980.0000.129-0.0030.1080.0970.0730.0760.0360.112
systolic-0.0000.1410.0750.2450.305-0.049-0.0441.0000.7470.2250.0670.186-0.1080.0640.174-0.0440.0680.1270.1690.2310.0400.0420.0310.109
relaxation0.0010.0660.1180.2650.2880.0090.0160.7471.0000.1990.0990.219-0.1210.0860.229-0.0340.0950.1230.1870.2650.0150.0200.0240.119
fasting blood sugar0.0000.2060.0370.1760.252-0.060-0.0550.2250.1991.0000.0710.242-0.1400.0540.110-0.0320.0890.0680.1510.2510.0290.0290.0150.065
Cholesterol-0.0010.120-0.1060.0220.080-0.015-0.0120.0670.0990.0711.0000.2400.1580.8780.022-0.042-0.0070.0860.1040.1360.0420.0390.0360.065
triglyceride-0.001-0.0320.2720.4300.4400.0690.0790.1860.2190.2420.2401.000-0.5510.0940.366-0.0690.2230.1080.3380.4600.0460.0420.0400.329
HDL0.0040.059-0.303-0.444-0.442-0.051-0.057-0.108-0.121-0.1400.158-0.5511.000-0.071-0.3380.026-0.259-0.069-0.297-0.2810.0280.0270.0480.268
LDL-0.0020.124-0.0780.0710.134-0.024-0.0220.0640.0860.0540.8780.094-0.0711.0000.043-0.0150.0350.0970.1320.1020.0000.0000.0000.011
hemoglobin0.001-0.3240.5860.5600.4190.1910.1980.1740.2290.1100.0220.366-0.3380.0431.000-0.0190.4960.2060.4230.4770.0600.0580.0810.457
Urine protein-0.002-0.025-0.012-0.036-0.042-0.0010.000-0.044-0.034-0.032-0.042-0.0690.026-0.015-0.0191.000-0.023-0.056-0.059-0.0610.0170.0160.0140.032
serum creatinine-0.003-0.1820.4820.4340.3110.1260.1290.0680.0950.089-0.0070.223-0.2590.0350.496-0.0231.0000.1410.2530.3180.0140.0090.0020.117
AST0.0020.0700.0700.1820.223-0.006-0.0030.1270.1230.0680.0860.108-0.0690.0970.206-0.0560.1411.0000.7070.3910.0000.0000.0060.007
ALT-0.000-0.1310.3000.4770.4680.1020.1080.1690.1870.1510.1040.338-0.2970.1320.423-0.0590.2530.7071.0000.5810.0150.0160.0000.001
Gtp0.002-0.0760.3490.4780.4730.0880.0970.2310.2650.2510.1360.460-0.2810.1020.477-0.0610.3180.3910.5811.0000.0030.0070.0220.131
hearing(left)0.0000.2750.1050.0700.0270.0780.0730.0400.0150.0290.0420.0460.0280.0000.0600.0170.0140.0000.0150.0031.0000.5530.0190.038
hearing(right)0.0040.2780.1070.0700.0270.0790.0760.0420.0200.0290.0390.0420.0270.0000.0580.0160.0090.0000.0160.0070.5531.0000.0160.037
dental caries0.0030.1290.1030.0890.0520.0310.0360.0310.0240.0150.0360.0400.0480.0000.0810.0140.0020.0060.0000.0220.0190.0161.0000.107
smoking0.0040.2170.4830.3700.2670.1060.1120.1090.1190.0650.0650.3290.2680.0110.4570.0320.1170.0070.0010.1310.0380.0370.1071.000

Missing values

2023-11-05T23:13:44.418579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-05T23:13:44.937006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtpdental cariessmoking
00551656081.00.50.6111358794172300407516.511.022252701
11701656589.00.60.72214683147194555712616.211.127233710
22201707581.00.40.5111187579178197459317.410.827315301
333518095105.01.51.21113188911802033810215.911.020273010
44301656080.51.51.011121769115587449315.410.819131701
55501705551.01.21.21114695101199343319915.910.7244211911
66451605569.01.51.21115088842221536912213.010.717121600
77551556084.50.70.911137911002821655119814.510.716151600
88401657089.00.71.011130801042431635915015.710.924213101
99401555073.01.51.5111057064183275512213.210.722161400
idageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtpdental cariessmoking
159246159246401656070.00.91.21110360932052584411013.710.7188700
159247159247201705571.01.21.5111107614217965739315.910.918181600
15924815924830170100100.01.01.011143951011871693911416.211.324433500
159249159249401708591.41.20.91111068912202484512516.110.926443701
159250159250251809088.01.51.21111968962061994312315.311.124294210
159251159251401554569.01.52.0111278064238477215914.510.825261300
159252159252501557582.01.01.01112080892132026410814.510.621201800
159253159253401605066.01.51.011114708418945879310.910.61591200
159254159254501657592.01.21.01112190122165148558014.411.122173701
159255159255401454576.41.01.211125878318687878114.010.821161700